Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	71,267
2	South Dakota	62,627
3	Iowa	49,312
4	Wisconsin	48,417
5	Nebraska	43,408
6	Mississippi	42,568
7	Alabama	41,541
8	Louisiana	41,512
9	Utah	41,367
10	Idaho	40,967
11	Tennessee	40,571
12	Arkansas	40,382
13	Florida	39,291
14	Illinois	38,587
15	Montana	37,125
16	Georgia	36,790
17	South Carolina	36,064
18	Nevada	35,812
19	Arizona	35,624
20	Missouri	35,285
21	Texas	35,036
22	Oklahoma	34,434
23	Rhode Island	34,341
24	Kansas	34,258
25	Minnesota	32,079
26	Indiana	31,623
27	Wyoming	29,908
28	New Jersey	28,866
29	North Carolina	27,977
30	Kentucky	27,574
31	New York	27,438
32	Delaware	27,319
33	Alaska	26,945
34	New Mexico	26,173
35	Maryland	25,547
36	District of Columbia	25,506
37	Massachusetts	24,867
38	California	24,773
39	Michigan	22,938
40	Colorado	22,899
41	Puerto Rico	22,596
42	Virginia	22,514
43	Connecticut	21,912
44	Ohio	21,410
45	Pennsylvania	18,369
46	Washington	16,061
47	West Virginia	15,849
48	Oregon	11,960
49	Hawaii	11,347
50	New Hampshire	9,184
51	Maine	5,723
52	Vermont	3,833

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	North Dakota	1,954
2	South Dakota	1,602
3	Iowa	1,351
4	Wyoming	1,094
5	Wisconsin	1,045
6	Nebraska	1,026
7	Minnesota	945
8	Illinois	879
9	Utah	858
10	Montana	828
11	Alaska	750
12	Indiana	703
13	Missouri	630
14	Kansas	615
15	New Mexico	599
16	Idaho	598
17	Colorado	561
18	Oklahoma	537
19	Nevada	498
20	Arkansas	497
21	Tennessee	464
22	Ohio	430
23	Kentucky	413
24	Michigan	365
25	West Virginia	345
26	Mississippi	313
27	Alabama	307
28	Massachusetts	301
29	Arizona	295
30	Delaware	290
31	Puerto Rico	287
32	New Jersey	276
33	South Carolina	262
34	Texas	262
35	Florida	256
36	Pennsylvania	246
37	North Carolina	229
38	Maryland	222
39	Oregon	206
40	Rhode Island	198
41	Virginia	194
42	Georgia	191
43	New York	175
44	Washington	168
45	New Hampshire	166
46	Louisiana	153
47	California	151
48	District of Columbia	150
49	Maine	107
50	Connecticut	99
51	Hawaii	90
52	Vermont	47

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,849
2	New York	1,712
3	Massachusetts	1,472
4	Connecticut	1,310
5	Louisiana	1,298
6	Mississippi	1,156
7	Rhode Island	1,155
8	District of Columbia	926
9	Arizona	846
10	North Dakota	845
11	Illinois	832
12	Florida	797
13	Michigan	795
14	Georgia	793
15	South Carolina	783
16	Delaware	737
17	Pennsylvania	710
18	Maryland	696
19	Arkansas	690
20	Indiana	687
21	Texas	664
22	Alabama	628
23	South Dakota	605
24	Nevada	601
25	Iowa	584
26	New Mexico	533
27	Missouri	527
28	Tennessee	521
29	Minnesota	480
30	Ohio	471
31	California	454
32	North Carolina	441
33	Virginia	434
34	Montana	426
35	Colorado	420
36	Wisconsin	411
37	Kansas	400
38	Idaho	386
39	Kentucky	367
40	Nebraska	367
41	Oklahoma	363
42	New Hampshire	359
43	Washington	335
44	West Virginia	280
45	Puerto Rico	273
46	Utah	205
47	Wyoming	196
48	Oregon	173
49	Hawaii	155
50	Maine	113
51	Alaska	107
52	Vermont	94

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	South Dakota	20
2	North Dakota	18
3	Montana	14
4	Kansas	9
5	Wisconsin	7
6	Arkansas	5
7	Illinois	5
8	Indiana	5
9	Minnesota	5
10	Nebraska	5
11	New Mexico	5
12	Wyoming	5
13	Mississippi	4
14	Tennessee	4
15	West Virginia	4
16	Alabama	3
17	Arizona	3
18	Idaho	3
19	Iowa	3
20	Massachusetts	3
21	Michigan	3
22	Colorado	2
23	Florida	2
24	Georgia	2
25	Kentucky	2
26	Louisiana	2
27	Missouri	2
28	Nevada	2
29	Oklahoma	2
30	Pennsylvania	2
31	South Carolina	2
32	Texas	2
33	Utah	2
34	Connecticut	1
35	District of Columbia	1
36	Maryland	1
37	New York	1
38	North Carolina	1
39	Ohio	1
40	Oregon	1
41	Puerto Rico	1
42	Alaska	0
43	California	0
44	Delaware	0
45	Hawaii	0
46	Maine	0
47	New Hampshire	0
48	New Jersey	0
49	Rhode Island	0
50	Vermont	0
51	Virginia	0
52	Washington	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Lincoln	Arkansas	180,820	1	99
Norton	Kansas	174,221	2	99
Chattahoochee	Georgia	170,991	3	99
Bon Homme	South Dakota	167,367	4	99
Trousdale	Tennessee	162,354	5	99
Davidson	Tennessee	53,352	360	88
Richland	South Carolina	43,807	664	78
York	South Carolina	26,646	1734	44
Orange	California	20,251	2193	30
Pierce	Washington	14,333	2587	17

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	7,208	1	99
Jerauld	South Dakota	6,458	2	99
Hancock	Georgia	5,321	3	99
Emporia city	Virginia	5,238	4	99
Kenedy	Texas	4,950	5	99
Richland	South Carolina	671	979	68
Davidson	Tennessee	526	1289	58
Orange	California	475	1431	54
York	South Carolina	399	1637	47
Pierce	Washington	296	1941	38

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.